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Record W2100392878 · doi:10.1002/elps.200800581

Highly resolved separation and sensitive amperometric detection of amino acids with an assembled microfluidic device

2009· article· en· W2100392878 on OpenAlexaff
Chun Zhai, Qiang Wei, Jianping Lei, Huangxian Ju

Bibliographic record

VenueElectrophoresis · 2009
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
KeywordsDetection limitChromatographyAmperometryAmino acidPhenylalanineValineMicrofluidicsChemistryAlanineCapillary electrophoresisCapillary actionAnalytical Chemistry (journal)Materials scienceElectrodeNanotechnologyBiochemistryElectrochemistry

Abstract

fetched live from OpenAlex

An assembled microfluidic device (AMD) was constructed for highly resolved separation and sensitive amperometric detection of amino acids. In the AMD three polymer retainers were integrated with a quartz capillary and a copper microdisk electrode. A facile ultrasonic method was suggested for the preparation of sampling fracture on the capillary. The fracture led to separation efficiency up to 340,000 plates/m and improved greatly the sensitivity for amino acid detection in an 8 cm long quartz capillary. At a sampling voltage of 100 V for 2 s and a separation voltage of 1800 V (225 V/cm) within 300 s, the linear ranges of 12 amino acids determination at +0.8 V (versus Ag/AgCl) were from 12, 20, 17, 27, 31, 44, 25 and 45 to 750 microM for tryptophan, phenylalanine, methionine, valine, threonine, alanine, serine and glycin, and 4.6, 17, 16 and 49 microM to 1.0 mM for lysine, proline, leucine and tyrosine with the detection limit (S/N=3) down to 1.4 microM for lysine. The designed AMD could be successfully applied to analyze amino acids in beverage samples with recovery from 97.8 to 105.2%, indicating its advantages and potential analytical application in different fields.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.215
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2009
Admission routes1
Has abstractyes

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